Lifeboat Foundation

While the world has been captivated by recent advances in artificial intelligence, researchers at Johns Hopkins University have identified a new form of intelligence: organoid intelligence. A future where computers are powered by lab-grown brain cells may be closer than we could ever have imagined.

What is an organoid? Organoids are three-dimensional tissue cultures commonly derived from human pluripotent stem cells. What looks like a clump of cells can be engineered to function like a human organ, mirroring its key structural and biological characteristics. Under the right laboratory conditions, genetic instructions from donated stem cells allow organoids to self-organize and grow into any type of organ tissue, including the human brain.

Although this may sound like science-fiction, brain organoids have been used to model and study neurodegenerative diseases for nearly a decade. Emerging studies now reveal that these lab grown brain cells may be capable of learning. In fact, a research team from Melbourne recently reported that they trained 800,000 brain cells to perform the computer game, Pong (see video). As this field of research continues to grow, researchers speculate that this so-called “intelligence in a dish” may be able to outcompete artificial intelligence.

The first deliveries of the Tesla Cybertruck are expected to take place later this year, and there are still a handful of unknowns about the futuristic truck. In recent weeks, however, Tesla CEO Elon Musk shared some details about the vehicle, alongside some included in the automaker’s latest Master Plan.

In its Master Plan 3 unveiled on April 5, Tesla stated that the Cybertruck will have a 100 kWh battery pack. However, it’s not clear if this refers to a base model or another specific variant, as reported by The Street. The battery pack size is the same as those of the Model S and X, Tesla’s premium-level sedan and SUV, despite the truck being a wider and heavier vehicle than these.

Cybertruck rivals in the electric pickup sector include the Rivian R1T and the Ford F-150 Lightning, which feature 135 kWh and 131 kWh battery packs, respectively. The Cybertruck will also include a 3,500-pound payload capacity, adjustable air suspension, and lockable exterior storage measuring about 100 cubic feet.

Greg Rutkowski is a more popular prompt for text-to-image AI art generators than Picasso.

Sales teams have typically not been early adopters of technology, but generative AI may be an exception to that. Sales work typically requires administrative work, routine interactions with clients, and management attention to tasks such as forecasting. AI can help do these tasks more quickly, which is why Microsoft and Salesforce have already rolled out sales-focused versions of this powerful tool.

Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.351825 data-title= How Generative AI Will Change Sales data-url=/2023/03/how-generative-ai-will-change-sales data-topic= Sales data-authors= Prabhakant Sinha; Arun Shastri; Sally E. Lorimer data-content-type= Digital Article data-content-image=/resources/images/article_assets/2023/03/Mar23_28_1422490566-383x215.jpg data-summary=

Microsoft and Salesforce have already rolled out sales-focused versions of this powerful tool.

The protective effects of vaccines have particularly been highlighted during the recent COVID-19 pandemic. Countries able to offer the vaccine demonstrate lowered infection rates and have kick-started the recovery of their economies.

The COVID-19 pandemic has also highlighted the need to proactively develop medical countermeasures to novel pathogens, in addition to advancing supply and manufacturing capacities to meet global demands.

Investing in vaccine manufacturing has both economic and societal benefits, in addition to protecting human health and limiting infection spread.

A stream of air bubbles can be most effective at cleaning produce or industrial equipment if it strikes at the correct angle.

Researchers believe that washing vegetables and food-processing equipment with flowing liquids filled with air bubbles could be effective, but little is known about how to optimize the process. Now engineers, using experiments and simulations, have shown that bubbles exert an optimal cleaning effect if they strike a surface at an angle of about 22.5° [1]. The researchers hope that this insight will help improve methods for the gentle cleaning of fruits and vegetables, potentially leading to a commercial food-cleaning device that they call a “fruit Jacuzzi.”

As bioengineer Sunghwan “Sunny” Jung of Cornell University points out, bubbles injected into fluid have long been used to clean biofilm-encrusted surfaces in settings such as wastewater treatment facilities. Experts generally believe that the technique works because bubbles flowing over a surface exert a shear force, parallel to the surface, which tends to remove attached contaminants. “It’s similar to how you move your hand along the surface of your skin when you’re cleaning your body, applying a shearing force at the surface,” says Jung. Even so, he says, little is known about the basic science behind the effect and in particular about how the motions of bubbles within the liquid might optimize the cleaning.

Topological superconductors are superconducting materials with unique characteristics, including the appearance of so-called in-gap Majorana states. These bound states can serve as qubits, making topological superconductors particularly promising for the creation of quantum computing technologies.

Some physicists have recently been exploring the potential for creating quantum systems that integrate superconductors with swirling configurations of atomic magnetic dipoles (spins), known as quantum skyrmion crystals. Most of these efforts suggested sandwiching quantum skyrmion crystals between superconductors to achieve topological superconductivity.

Kristian Mæland and Asle Sudbø, two researchers at the Norwegian University of Science and Technology, have recently proposed an alternative model system of topological superconductivity, which does not contain superconducting materials. This theoretical model, introduced in Physical Review Letters, would instead use a sandwich structure of a heavy metal, a magnetic insulator, and a normal metal, where the heavy metal induces a quantum skyrmion crystal in the magnetic insulator.

Advances in quantum computation for electronic structure, and particularly heuristic quantum algorithms, create an ongoing need to characterize the performance and limitations of these methods. Here we discuss some potential pitfalls connected with the use of hardware-efficient Ansätze in variational quantum simulations of electronic structure. We illustrate that hardware-efficient Ansätze may break Hamiltonian symmetries and yield nondifferentiable potential energy curves, in addition to the well-known difficulty of optimizing variational parameters. We discuss the interplay between these limitations by carrying out a comparative analysis of hardware-efficient Ansätze versus unitary coupled cluster and full configuration interaction, and of second-and first-quantization strategies to encode Fermionic degrees of freedom to qubits.

Computer simulations of a human brain under the influence of LSD show that entropy increases the most in regions responsible for processing vision and integrating sensory information.

By Karmela Padavic-Callaghan